At the moment LOBSTER data available for 1520 compounds.
All the computational data (including raw unprocessed data) is available to download from our Zenodo communuity : https://zenodo.org/communities/automated-bonding-analysis/?page=1&size=20
Dataset size:
| Root Keys | Data type | Description |
|---|---|---|
| all_bonds | dict | Summarized relevant bonds data (See table 2 of the manuscript for details) |
| cation_anion_bonds | dict | Summarized relevant cation-anion bonds data (See table 2 of the manuscript forfor details) |
| madelung_energies | dict | Total electrostatic energy for the structure as calculated from the Mulliken and Löwdin charges |
| charges | dict | Atomic charges with Mulliken and Löwdin population analysis methods as keys. Each keys corresponding list follows the order of sites in the crystal structure. |
| calc_quality_summary | dict | Dict summarizing results of technical validation tests like charge spillings, band overlaps, DOS and charge comparisons |
| Root Keys | Data type | Description |
|---|---|---|
| structure | dict | Dict representation of the pymatgen structure object used for the LOBSTER calculation |
| charges | dict | Atomic charges dict from LOBSTER based on Mulliken and Loewdin charge analysis |
| lobsterin | dict | LOBSTER calculation inputs |
| lobsterout | dict | Information of LOBSTER calculation output |
| lobsterpy_data | dict | Summarized bonding analysis data from Lobsterpy (all bonds mode). It also includes Cohp objects to plot the COHP curves from the automatic analysis |
| lobsterpy_text | dict | LobsterPy automatic analysis summary text (all bonds mode) |
| strongest_bonds_icohp | dict | Describes the strongest ICOHP bonds |
| strongest_bonds_icoop | dict | Describes the strongest ICOOP bonds |
| strongest_bonds_icobi | dict | Describes the strongest ICOBI bonds |
| lobsterpy_data_cation_anion | dict | Summarized bonding analysis data from Lobsterpy (cation-anion bonds mode). It also includes Cohp objects to plot the COHP curves from the automatic analysis |
| lobsterpy_text_cation_anion | dict | LobsterPy automatic analysis summary text (cation-anion bonds mode) |
| strongest_bonds_icohp_cation_anion | dict | Describes the strongest cation-anion ICOHP bonds |
| strongest_bonds_icoop_cation_anion | dict | Describes the strongest cation-anion ICOOP bonds |
| strongest_bonds_icobi_cation_anion | dict | Describes the strongest cation-anion ICOBI bonds |
| cohp_data | dict | Dict representation of pymatgen CompleteCohp object including data to plot COHP curves |
| coop_data | dict | Dict representation of pymatgen CompleteCohp object including data to plot COOP curves |
| cobi_data | dict | Dict representation of pymatgen CompleteCohp object including data to plot COBI curves |
| dos | dict | Dict representation of pymatgen LobsterCompleteDos object including the DOSCAR.lobster data |
| lso_dos | dict | Dict representation of pymatgen LobsterCompleteDos object including the DOSCAR.LSO.lobster data |
| madelung_energies | dict | Consists of the Madelung energies of the structure derived from the Mulliken and Löwdin charges |
| site_potentials | dict | Site potentials dict based on Mulliken and Loewdin charges |
| gross_populations | dict | Gross populations dict based on Mulliken and Loewdin charges with each site as a key and the gross population as a value. |
| band_overlaps | dict | Band overlaps data for each k-point from bandOverlaps.lobster file if it exists |
Parsers for all the LOBSTER output files are already implemented in pymatgen namely :
Plotters for DOSCAR, COHP(ICOHP)/COBI (ICOBI)/COOP (ICOOP) are also readily available. Pymatgen DOS plotter now also has option to plot DOS in convention followed in chemistry: https://github.com/materialsproject/pymatgen/blob/d621490d5d6742b4c7e432d3144dec6858236c79/pymatgen/electronic_structure/plotter.py#L131
Interactive plotter for COHP/ICOHP are being implemented in Lobsterpy package (Useful for website)
We have implemented methods in pymatgen that can easily compare DOS using Tanimoto index as similarity measure. (Could be useful to demonstrate it on website) :
Compare two DOS fingerprints (Tanimoto index): https://github.com/materialsproject/pymatgen/blob/d621490d5d6742b4c7e432d3144dec6858236c79/pymatgen/electronic_structure/dos.py#L1284
We also have created a dash app that helps in visualizing the DOS benchmark for our dataset and is available on zenodo : https://doi.org/10.5281/zenodo.7795903